Solution structure of the N-terminal dsRBD of Drosophila ADAR and interaction studies with RNA

Adenosine deaminases that act on RNA (ADAR) catalyze adenosine to inosine (A-to-I) editing in double-stranded RNA (dsRNA) substrates. Inosine is read as guanosine by the translation machinery; therefore A-to-I editing events in coding sequences may result in recoding genetic information. Whereas vertebrates have two catalytically active enzymes, namely ADAR1 and ADAR2, Drosophila has a single ADAR protein (dADAR) related to ADAR2. The structural determinants controlling substrate recognition and editing of a specific adenosine within dsRNA substrates are only partially understood. Here, we report the solution structure of the N-terminal dsRNA binding domain (dsRBD) of dADAR and use NMR chemical shift perturbations to identify the protein surface involved in RNA binding. Additionally, we show that Drosophila ADAR edits the R/G site in the mammalian GluR-2 pre-mRNA which is naturally modified by both ADAR1 and ADAR2. We then constructed a model showing how dADAR dsRBD1 binds to the GluR-2 R/G stem-loop. This model revealed that most side chains interacting with the RNA sugar-phosphate backbone need only small displacement to adapt for dsRNA binding and are thus ready to bind to their dsRNA target. It also predicts that dADAR dsRBD1 would bind to dsRNA with less sequence specificity than dsRBDs of ADAR2. Altogether, this study gives new insights into dsRNA substrate recognition by Drosophila ADAR

Mapping Site-Specific Changes that Affect Stability of the NTerminal Domain of Calmodulin

Biophysical tools have been invaluable in formulating therapeutic proteins. These tools characterize protein stability rapidly in a variety of solution conditions, but in general, the techniques lack the ability to discern site-specific information to probe how solution environment acts to stabilize or destabilize the protein. NMR spectroscopy can provide site-specific information about subtle structural changes of a protein under different conditions, enabling one to assess the mechanism of protein stabilization. In this study, NMR was employed to detect structural perturbations at individual residues as a result of altering pH and ionic strength. The N-terminal domain of calmodulin (N-CaM) was used as a model system, and the 1H-15N heteronuclear single quantum coherence (HSQC) experiment was used to investigate effects of pH and ionic strength on individual residues. NMR analysis revealed that different solution conditions affect individual residues differently, even when the amino acid sequence and structure are highly similar. This study shows that addition of NMR to the formulation toolbox has the ability to extend understanding of the relationship between site-specific changes and overall protein stability

A Novel System of Polymorphic and Diverse NK Cell Receptors in Primates

There are two main classes of natural killer (NK) cell receptors in mammals, the killer cell immunoglobulin-like receptors (KIR) and the structurally unrelated killer cell lectin-like receptors (KLR). While KIR represent the most diverse group of NK receptors in all primates studied to date, including humans, apes, and Old and New World monkeys, KLR represent the functional equivalent in rodents. Here, we report a first digression from this rule in lemurs, where the KLR (CD94/NKG2) rather than KIR constitute the most diverse group of NK cell receptors. We demonstrate that natural selection contributed to such diversification in lemurs and particularly targeted KLR residues interacting with the peptide presented by MHC class I ligands. We further show that lemurs lack a strict ortholog or functional equivalent of MHC-E, the ligands of non-polymorphic KLR in “higher” primates. Our data support the existence of a hitherto unknown system of polymorphic and diverse NK cell receptors in primates and of combinatorial diversity as a novel mechanism to increase NK cell receptor repertoire

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment